Ragoza Matthew, Masuda Tomohide, Koes David Ryan
Intelligent Systems Program, University of Pittsburgh Pittsburgh PA 15213 USA
Department of Computational and Systems Biology, University of Pittsburgh Pittsburgh PA 15213 USA
Chem Sci. 2022 Feb 7;13(9):2701-2713. doi: 10.1039/d1sc05976a. eCollection 2022 Mar 2.
The goal of structure-based drug discovery is to find small molecules that bind to a given target protein. Deep learning has been used to generate drug-like molecules with certain cheminformatic properties, but has not yet been applied to generating 3D molecules predicted to bind to proteins by sampling the conditional distribution of protein-ligand binding interactions. In this work, we describe for the first time a deep learning system for generating 3D molecular structures conditioned on a receptor binding site. We approach the problem using a conditional variational autoencoder trained on an atomic density grid representation of cross-docked protein-ligand structures. We apply atom fitting and bond inference procedures to construct valid molecular conformations from generated atomic densities. We evaluate the properties of the generated molecules and demonstrate that they change significantly when conditioned on mutated receptors. We also explore the latent space learned by our generative model using sampling and interpolation techniques. This work opens the door for end-to-end prediction of stable bioactive molecules from protein structures with deep learning.
基于结构的药物发现的目标是找到与给定靶蛋白结合的小分子。深度学习已被用于生成具有某些化学信息学特性的类药物分子，但尚未应用于通过对蛋白质-配体结合相互作用的条件分布进行采样来生成预测与蛋白质结合的3D分子。在这项工作中，我们首次描述了一种基于受体结合位点生成3D分子结构的深度学习系统。我们使用在交叉对接的蛋白质-配体结构的原子密度网格表示上训练的条件变分自编码器来解决这个问题。我们应用原子拟合和键推断程序从生成的原子密度构建有效的分子构象。我们评估生成分子的性质，并证明当以突变受体为条件时它们会发生显著变化。我们还使用采样和插值技术探索了我们的生成模型学习到的潜在空间。这项工作为利用深度学习从蛋白质结构进行稳定生物活性分子的端到端预测打开了大门。
